{
 "cells": [
  {
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-12-15T13:19:08.879292Z",
     "start_time": "2024-12-15T13:19:06.989253Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 导入必要的库\n",
    "import tensorflow as tf\n",
    "from keras.applications.resnet import ResNet152\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras.applications import InceptionV3, ResNet152\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Model\n",
    "from keras.layers import Flatten, GlobalAveragePooling2D, Reshape, Dense, multiply, Lambda\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import keras.backend as K\n",
    "from tensorflow.keras import regularizers\n"
   ],
   "id": "initial_id",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T13:19:09.348976Z",
     "start_time": "2024-12-15T13:19:09.336261Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Define paths for your dataset #plt.savefig(os.path.join(save_folder, f'image_{i}.png'), dpi=600, bbox_inches='tight')\n",
    "train_dir = r'D:\\ProgramData\\CDUT\\mv\\cw\\MVcw\\data_mv\\dataset\\train'\n",
    "test_dir = r'D:\\ProgramData\\CDUT\\mv\\cw\\MVcw\\data_mv\\dataset\\test'"
   ],
   "id": "34bbc8c6c195206f",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T13:19:10.826593Z",
     "start_time": "2024-12-15T13:19:10.808338Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def load_data():\n",
    "    train_datagen = ImageDataGenerator(\n",
    "        rotation_range=40,\n",
    "        width_shift_range=0.2,\n",
    "        height_shift_range=0.2,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True,\n",
    "        fill_mode='nearest',\n",
    "        validation_split=0.2\n",
    "    )\n",
    "    \n",
    "    train_generator = train_datagen.flow_from_directory(\n",
    "        train_dir,\n",
    "        target_size=(128, 128),\n",
    "        batch_size=16,\n",
    "        class_mode='categorical',\n",
    "        subset='training'\n",
    "    )\n",
    "    print(\"Train generator loaded:\", train_generator.samples)\n",
    "    \n",
    "    validation_generator = train_datagen.flow_from_directory(\n",
    "        train_dir,\n",
    "        target_size=(128, 128),\n",
    "        batch_size=16,\n",
    "        class_mode='categorical',\n",
    "        subset='validation'\n",
    "    )\n",
    "    print(\"Validation generator loaded:\", validation_generator.samples)\n",
    "    \n",
    "    test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "    \n",
    "    test_generator = test_datagen.flow_from_directory(\n",
    "        test_dir,\n",
    "        target_size=(128, 128),\n",
    "        batch_size=16,\n",
    "        class_mode='categorical'\n",
    "    )\n",
    "    print(\"Test generator loaded:\", test_generator.samples)\n",
    "    \n",
    "    return train_generator, validation_generator, test_generator\n"
   ],
   "id": "44a441686f0d4383",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T09:44:57.031026Z",
     "start_time": "2024-12-15T09:44:57.024186Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 构建 Inception 模型\n",
    "\n",
    "def build_inception_model():\n",
    "    base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(128, 128, 3))\n",
    "    \n",
    "    # 解冻部分顶层\n",
    "    for layer in base_model.layers[:249]:\n",
    "        layer.trainable = False\n",
    "    for layer in base_model.layers[249:]:\n",
    "        layer.trainable = True\n",
    "    \n",
    "    x = base_model.output\n",
    "    x = layers.GlobalAveragePooling2D()(x)\n",
    "    x = layers.Dense(1024, activation='relu', kernel_regularizer=regularizers.l2(0.001))(x)\n",
    "    x = layers.Dropout(0.5)(x)  # 添加Dropout层\n",
    "    predictions = layers.Dense(train_generator.num_classes, activation='softmax')(x)\n",
    "    \n",
    "    model = models.Model(inputs=base_model.input, outputs=predictions)\n",
    "    return model\n"
   ],
   "id": "bf2be70c6142a4e1",
   "outputs": [],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T11:02:51.328170Z",
     "start_time": "2024-12-15T11:02:51.321305Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 构建 ResNet 模型\n",
    "def build_resnet_model():\n",
    "    base_model = ResNet152(weights='imagenet', include_top=False, input_shape=(128, 128, 3))\n",
    "    x = base_model.output\n",
    "    x = layers.GlobalAveragePooling2D()(x)\n",
    "    x = layers.Dense(1024, activation='relu')(x)\n",
    "    predictions = layers.Dense(train_generator.num_classes, activation='softmax')(x)\n",
    "    model = models.Model(inputs=base_model.input, outputs=predictions)\n",
    "    return model"
   ],
   "id": "29eac59b6c4c4753",
   "outputs": [],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T09:44:57.058371Z",
     "start_time": "2024-12-15T09:44:57.052510Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 构建包含 Depthwise Separable Convolution 的模型\n",
    "def build_depthwise_separable_model():\n",
    "    model = models.Sequential([\n",
    "        layers.Conv2D(32, (3, 3), strides=(2, 2), padding='same', input_shape=(128, 128, 3)),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.ReLU(),\n",
    "        layers.DepthwiseConv2D((3, 3), strides=(1, 1), padding='same'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.ReLU(),\n",
    "        layers.GlobalAveragePooling2D(),\n",
    "        layers.Dense(1024, activation='relu'),\n",
    "        layers.Dense(train_generator.num_classes, activation='softmax')\n",
    "    ])\n",
    "    return model"
   ],
   "id": "9e6ff830aa6a392a",
   "outputs": [],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T15:35:16.905206Z",
     "start_time": "2024-12-15T15:35:16.895427Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def build_attention_model():\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, input_shape=(128, 128, 3))\n",
    "    x = Flatten()(base_model.output)\n",
    "    x = Dense(32, activation='relu')(x)\n",
    "    attention = Dense(1, activation='tanh')(x)\n",
    "    attention = Reshape((-1, 1, 1))(attention)\n",
    "    attention = multiply([x, attention])\n",
    "    attention = Lambda(lambda x: K.sum(x, axis=-2))(attention)\n",
    "    attention = Lambda(lambda x: K.expand_dims(x, axis=-1))(attention)\n",
    "    attention = GlobalAveragePooling2D()(attention)\n",
    "    merged = Dense(2, activation='softmax')(attention)  \n",
    "    model = Model(inputs=base_model.input, outputs=merged)\n",
    "    for layer in base_model.layers:\n",
    "        layer.trainable = False\n",
    "    return model\n",
    "   "
   ],
   "id": "3fe8c080aa4be132",
   "outputs": [],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T13:19:26.183213Z",
     "start_time": "2024-12-15T13:19:26.174222Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 训练和评估模型\n",
    "def train_model(model, train_generator, validation_generator):\n",
    "    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "    history = model.fit(\n",
    "        train_generator,\n",
    "        epochs=20,\n",
    "        validation_data=validation_generator,\n",
    "        callbacks=[early_stopping]\n",
    "    )\n",
    "    return history"
   ],
   "id": "e0ea12c3ca7a55b",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T13:19:28.656031Z",
     "start_time": "2024-12-15T13:19:28.648057Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "# 绘制训练历史\n",
    "def plot_history(history, model_name):\n",
    "    plt.figure(figsize=(12, 4))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history['accuracy'], label='train accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
    "    plt.title(f'{model_name} Accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend(loc='lower right')\n",
    "    \n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['loss'], label='train loss')\n",
    "    plt.plot(history.history['val_loss'], label='val loss')\n",
    "    plt.title(f'{model_name} Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend(loc='upper right')\n",
    "    \n",
    "    plt.show()\n"
   ],
   "id": "5ea42c338a6c6a6f",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T13:19:31.224633Z",
     "start_time": "2024-12-15T13:19:30.877950Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 主函数\n",
    "if __name__ == \"__main__\":\n",
    "    train_generator, validation_generator, test_generator = load_data()\n",
    "        # 获取 num_classes\n",
    "    num_classes = train_generator.num_classes\n",
    "    "
   ],
   "id": "c4489785ce0f2bd8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 674 images belonging to 2 classes.\n",
      "Train generator loaded: 674\n",
      "Found 168 images belonging to 2 classes.\n",
      "Validation generator loaded: 168\n",
      "Found 842 images belonging to 2 classes.\n",
      "Test generator loaded: 842\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T10:28:00.277363Z",
     "start_time": "2024-12-15T09:44:57.453257Z"
    }
   },
   "cell_type": "code",
   "source": [
    "    # Inception 模型\n",
    "    inception_model = build_inception_model()\n",
    "    optimizer = Adam(learning_rate=0.0001)\n",
    "    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.00001)\n",
    "    inception_model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "    print(\"Training Inception Model...\")\n",
    "    inception_history = inception_model.fit(train_generator, epochs=50, validation_data=validation_generator, callbacks=[reduce_lr, early_stopping])\n",
    "    plot_history(inception_history, 'InceptionV3')\n",
    "    "
   ],
   "id": "9b5d79f0ecc2ba67",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Inception Model...\n",
      "Epoch 1/50\n",
      "43/43 [==============================] - 65s 1s/step - loss: 2.0898 - accuracy: 0.6424 - val_loss: 3.3915 - val_accuracy: 0.5893\n",
      "Epoch 2/50\n",
      "43/43 [==============================] - 55s 1s/step - loss: 1.9892 - accuracy: 0.6780 - val_loss: 2.7619 - val_accuracy: 0.6012\n",
      "Epoch 3/50\n",
      "43/43 [==============================] - 53s 1s/step - loss: 1.9292 - accuracy: 0.6929 - val_loss: 2.4164 - val_accuracy: 0.6071\n",
      "Epoch 4/50\n",
      "43/43 [==============================] - 54s 1s/step - loss: 1.9464 - accuracy: 0.6825 - val_loss: 2.4220 - val_accuracy: 0.6071\n",
      "Epoch 5/50\n",
      "43/43 [==============================] - 53s 1s/step - loss: 1.8430 - accuracy: 0.7196 - val_loss: 2.2641 - val_accuracy: 0.6190\n",
      "Epoch 6/50\n",
      "43/43 [==============================] - 53s 1s/step - loss: 1.8716 - accuracy: 0.7166 - val_loss: 1.8331 - val_accuracy: 0.7500\n",
      "Epoch 7/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.7810 - accuracy: 0.7181 - val_loss: 2.2315 - val_accuracy: 0.6131\n",
      "Epoch 8/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.7629 - accuracy: 0.7374 - val_loss: 2.0101 - val_accuracy: 0.6310\n",
      "Epoch 9/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.7399 - accuracy: 0.7389 - val_loss: 1.8466 - val_accuracy: 0.6726\n",
      "Epoch 10/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.7249 - accuracy: 0.7359 - val_loss: 2.0531 - val_accuracy: 0.6488\n",
      "Epoch 11/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.6635 - accuracy: 0.7493 - val_loss: 1.7102 - val_accuracy: 0.7083\n",
      "Epoch 12/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.6282 - accuracy: 0.7656 - val_loss: 1.7291 - val_accuracy: 0.7143\n",
      "Epoch 13/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.6319 - accuracy: 0.7671 - val_loss: 1.5663 - val_accuracy: 0.7560\n",
      "Epoch 14/50\n",
      "43/43 [==============================] - 52s 1s/step - loss: 1.6013 - accuracy: 0.7596 - val_loss: 1.5797 - val_accuracy: 0.7738\n",
      "Epoch 15/50\n",
      "43/43 [==============================] - 52s 1s/step - loss: 1.5457 - accuracy: 0.7804 - val_loss: 1.6390 - val_accuracy: 0.7083\n",
      "Epoch 16/50\n",
      "43/43 [==============================] - 53s 1s/step - loss: 1.5242 - accuracy: 0.7864 - val_loss: 1.4789 - val_accuracy: 0.7976\n",
      "Epoch 17/50\n",
      "43/43 [==============================] - 52s 1s/step - loss: 1.5138 - accuracy: 0.8012 - val_loss: 1.7736 - val_accuracy: 0.6131\n",
      "Epoch 18/50\n",
      "43/43 [==============================] - 53s 1s/step - loss: 1.4959 - accuracy: 0.7923 - val_loss: 1.9895 - val_accuracy: 0.5952\n",
      "Epoch 19/50\n",
      "43/43 [==============================] - 53s 1s/step - loss: 1.4708 - accuracy: 0.7864 - val_loss: 1.6900 - val_accuracy: 0.6429\n",
      "Epoch 20/50\n",
      "43/43 [==============================] - 54s 1s/step - loss: 1.4595 - accuracy: 0.7864 - val_loss: 1.5328 - val_accuracy: 0.7500\n",
      "Epoch 21/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.4556 - accuracy: 0.7819 - val_loss: 1.6747 - val_accuracy: 0.6548\n",
      "Epoch 22/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.4097 - accuracy: 0.7982 - val_loss: 1.4787 - val_accuracy: 0.7560\n",
      "Epoch 23/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.3699 - accuracy: 0.8116 - val_loss: 1.5537 - val_accuracy: 0.7202\n",
      "Epoch 24/50\n",
      "43/43 [==============================] - 52s 1s/step - loss: 1.3710 - accuracy: 0.8101 - val_loss: 1.5079 - val_accuracy: 0.7381\n",
      "Epoch 25/50\n",
      "43/43 [==============================] - 54s 1s/step - loss: 1.3467 - accuracy: 0.8160 - val_loss: 1.4807 - val_accuracy: 0.7440\n",
      "Epoch 26/50\n",
      "43/43 [==============================] - 53s 1s/step - loss: 1.3498 - accuracy: 0.8323 - val_loss: 1.4659 - val_accuracy: 0.7738\n",
      "Epoch 27/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.3517 - accuracy: 0.8294 - val_loss: 1.4553 - val_accuracy: 0.7619\n",
      "Epoch 28/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.3625 - accuracy: 0.8056 - val_loss: 1.5549 - val_accuracy: 0.7321\n",
      "Epoch 29/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.3245 - accuracy: 0.8294 - val_loss: 1.5198 - val_accuracy: 0.7262\n",
      "Epoch 30/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.3352 - accuracy: 0.8205 - val_loss: 1.4878 - val_accuracy: 0.7738\n",
      "Epoch 31/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.3392 - accuracy: 0.8101 - val_loss: 1.4988 - val_accuracy: 0.7738\n",
      "Epoch 32/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.3506 - accuracy: 0.8086 - val_loss: 1.4886 - val_accuracy: 0.7500\n",
      "Epoch 33/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.3174 - accuracy: 0.8353 - val_loss: 1.4726 - val_accuracy: 0.7679\n",
      "Epoch 34/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.2842 - accuracy: 0.8442 - val_loss: 1.5432 - val_accuracy: 0.7381\n",
      "Epoch 35/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.2853 - accuracy: 0.8531 - val_loss: 1.4696 - val_accuracy: 0.7440\n",
      "Epoch 36/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.3323 - accuracy: 0.8160 - val_loss: 1.4914 - val_accuracy: 0.7381\n",
      "Epoch 37/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.2964 - accuracy: 0.8323 - val_loss: 1.4022 - val_accuracy: 0.8274\n",
      "Epoch 38/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.3164 - accuracy: 0.8175 - val_loss: 1.4585 - val_accuracy: 0.7500\n",
      "Epoch 39/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.2636 - accuracy: 0.8487 - val_loss: 1.5662 - val_accuracy: 0.7321\n",
      "Epoch 40/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.3248 - accuracy: 0.8309 - val_loss: 1.4508 - val_accuracy: 0.7381\n",
      "Epoch 41/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.2727 - accuracy: 0.8516 - val_loss: 1.4609 - val_accuracy: 0.7500\n",
      "Epoch 42/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.3077 - accuracy: 0.8131 - val_loss: 1.5437 - val_accuracy: 0.7262\n",
      "Epoch 43/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.2964 - accuracy: 0.8323 - val_loss: 1.3942 - val_accuracy: 0.7857\n",
      "Epoch 44/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.2889 - accuracy: 0.8323 - val_loss: 1.5339 - val_accuracy: 0.7262\n",
      "Epoch 45/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.2977 - accuracy: 0.8294 - val_loss: 1.4893 - val_accuracy: 0.7202\n",
      "Epoch 46/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.2712 - accuracy: 0.8412 - val_loss: 1.3941 - val_accuracy: 0.7798\n",
      "Epoch 47/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.2726 - accuracy: 0.8427 - val_loss: 1.4087 - val_accuracy: 0.7917\n",
      "Epoch 48/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.2682 - accuracy: 0.8427 - val_loss: 1.4421 - val_accuracy: 0.7262\n",
      "Epoch 49/50\n",
      "43/43 [==============================] - 51s 1s/step - loss: 1.2616 - accuracy: 0.8234 - val_loss: 1.5299 - val_accuracy: 0.7381\n",
      "Epoch 50/50\n",
      "43/43 [==============================] - 50s 1s/step - loss: 1.2312 - accuracy: 0.8576 - val_loss: 1.5184 - val_accuracy: 0.7262\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T11:16:18.527293Z",
     "start_time": "2024-12-15T11:16:17.737690Z"
    }
   },
   "cell_type": "code",
   "source": "    inception_model.save('inception_model.h5')",
   "id": "bba8fdf62befd63",
   "outputs": [],
   "execution_count": 41
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T10:46:15.436946Z",
     "start_time": "2024-12-15T10:28:00.308612Z"
    }
   },
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training ResNet Model...\n",
      "Epoch 1/20\n",
      "43/43 [==============================] - 57s 1s/step - loss: 1.0903 - accuracy: 0.7240 - val_loss: 2158.4075 - val_accuracy: 0.5952\n",
      "Epoch 2/20\n",
      "43/43 [==============================] - 52s 1s/step - loss: 0.4380 - accuracy: 0.8501 - val_loss: 505.6142 - val_accuracy: 0.5952\n",
      "Epoch 3/20\n",
      "43/43 [==============================] - 57s 1s/step - loss: 0.3317 - accuracy: 0.8798 - val_loss: 390.3721 - val_accuracy: 0.5952\n",
      "Epoch 4/20\n",
      "43/43 [==============================] - 57s 1s/step - loss: 0.2671 - accuracy: 0.8887 - val_loss: 43.1168 - val_accuracy: 0.8036\n",
      "Epoch 5/20\n",
      "43/43 [==============================] - 56s 1s/step - loss: 0.2461 - accuracy: 0.9021 - val_loss: 4.2814 - val_accuracy: 0.8512\n",
      "Epoch 6/20\n",
      "43/43 [==============================] - 56s 1s/step - loss: 0.1441 - accuracy: 0.9510 - val_loss: 9.8633 - val_accuracy: 0.6012\n",
      "Epoch 7/20\n",
      "43/43 [==============================] - 57s 1s/step - loss: 0.3518 - accuracy: 0.8754 - val_loss: 3.3777 - val_accuracy: 0.4702\n",
      "Epoch 8/20\n",
      "43/43 [==============================] - 56s 1s/step - loss: 0.2325 - accuracy: 0.9154 - val_loss: 6.6695 - val_accuracy: 0.6012\n",
      "Epoch 9/20\n",
      "43/43 [==============================] - 55s 1s/step - loss: 0.3996 - accuracy: 0.8398 - val_loss: 2.3356 - val_accuracy: 0.6012\n",
      "Epoch 10/20\n",
      "43/43 [==============================] - 54s 1s/step - loss: 0.2154 - accuracy: 0.9288 - val_loss: 8.3579 - val_accuracy: 0.6369\n",
      "Epoch 11/20\n",
      "43/43 [==============================] - 54s 1s/step - loss: 0.3010 - accuracy: 0.8991 - val_loss: 2.2239 - val_accuracy: 0.6429\n",
      "Epoch 12/20\n",
      "43/43 [==============================] - 53s 1s/step - loss: 0.2360 - accuracy: 0.9273 - val_loss: 0.9031 - val_accuracy: 0.6964\n",
      "Epoch 13/20\n",
      "43/43 [==============================] - 54s 1s/step - loss: 0.1427 - accuracy: 0.9496 - val_loss: 0.6085 - val_accuracy: 0.7976\n",
      "Epoch 14/20\n",
      "43/43 [==============================] - 53s 1s/step - loss: 0.2040 - accuracy: 0.9243 - val_loss: 2.6950 - val_accuracy: 0.5357\n",
      "Epoch 15/20\n",
      "43/43 [==============================] - 54s 1s/step - loss: 0.2063 - accuracy: 0.9214 - val_loss: 2.4841 - val_accuracy: 0.6071\n",
      "Epoch 16/20\n",
      "43/43 [==============================] - 53s 1s/step - loss: 0.1800 - accuracy: 0.9273 - val_loss: 73.7459 - val_accuracy: 0.3452\n",
      "Epoch 17/20\n",
      "43/43 [==============================] - 53s 1s/step - loss: 0.2298 - accuracy: 0.9392 - val_loss: 7.8757 - val_accuracy: 0.6369\n",
      "Epoch 18/20\n",
      "43/43 [==============================] - 52s 1s/step - loss: 0.1718 - accuracy: 0.9421 - val_loss: 0.2492 - val_accuracy: 0.9405\n",
      "Epoch 19/20\n",
      "43/43 [==============================] - 53s 1s/step - loss: 0.1426 - accuracy: 0.9451 - val_loss: 11.2491 - val_accuracy: 0.5952\n",
      "Epoch 20/20\n",
      "43/43 [==============================] - 53s 1s/step - loss: 0.1233 - accuracy: 0.9540 - val_loss: 2.8669 - val_accuracy: 0.6548\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 31,
   "source": [
    "   # ResNet 模型\n",
    "    resnet_model = build_resnet_model()\n",
    "    print(\"Training ResNet Model...\")\n",
    "    resnet_history = train_model(resnet_model, train_generator, validation_generator)\n",
    "    plot_history(resnet_history, 'ResNet50')\n",
    "    "
   ],
   "id": "2b111217ccd1713e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T11:50:06.742881Z",
     "start_time": "2024-12-15T11:50:05.429838Z"
    }
   },
   "cell_type": "code",
   "source": "    resnet_model.save('resnet_model.h5')",
   "id": "bb300396e2867692",
   "outputs": [],
   "execution_count": 43
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T11:00:08.756202Z",
     "start_time": "2024-12-15T10:46:15.458028Z"
    }
   },
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Depthwise Separable Convolution Model...\n",
      "Epoch 1/20\n",
      "43/43 [==============================] - 54s 1s/step - loss: 0.5569 - accuracy: 0.7062 - val_loss: 0.7172 - val_accuracy: 0.5952\n",
      "Epoch 2/20\n",
      "43/43 [==============================] - 51s 1s/step - loss: 0.4305 - accuracy: 0.8101 - val_loss: 0.5447 - val_accuracy: 0.6012\n",
      "Epoch 3/20\n",
      "43/43 [==============================] - 52s 1s/step - loss: 0.3650 - accuracy: 0.8591 - val_loss: 0.5142 - val_accuracy: 0.8214\n",
      "Epoch 4/20\n",
      "43/43 [==============================] - 52s 1s/step - loss: 0.2937 - accuracy: 0.8843 - val_loss: 0.4323 - val_accuracy: 0.7321\n",
      "Epoch 5/20\n",
      "43/43 [==============================] - 52s 1s/step - loss: 0.2883 - accuracy: 0.8828 - val_loss: 0.8831 - val_accuracy: 0.5833\n",
      "Epoch 6/20\n",
      "43/43 [==============================] - 51s 1s/step - loss: 0.2263 - accuracy: 0.9169 - val_loss: 0.3152 - val_accuracy: 0.8869\n",
      "Epoch 7/20\n",
      "43/43 [==============================] - 51s 1s/step - loss: 0.2926 - accuracy: 0.8769 - val_loss: 0.4000 - val_accuracy: 0.8095\n",
      "Epoch 8/20\n",
      "43/43 [==============================] - 52s 1s/step - loss: 0.2215 - accuracy: 0.9139 - val_loss: 0.4572 - val_accuracy: 0.7738\n",
      "Epoch 9/20\n",
      "43/43 [==============================] - 53s 1s/step - loss: 0.2456 - accuracy: 0.9080 - val_loss: 1.3183 - val_accuracy: 0.5298\n",
      "Epoch 10/20\n",
      "43/43 [==============================] - 53s 1s/step - loss: 0.1743 - accuracy: 0.9347 - val_loss: 1.5307 - val_accuracy: 0.4167\n",
      "Epoch 11/20\n",
      "43/43 [==============================] - 53s 1s/step - loss: 0.2187 - accuracy: 0.9258 - val_loss: 0.2678 - val_accuracy: 0.8988\n",
      "Epoch 12/20\n",
      "43/43 [==============================] - 52s 1s/step - loss: 0.2066 - accuracy: 0.9303 - val_loss: 1.0481 - val_accuracy: 0.5833\n",
      "Epoch 13/20\n",
      "43/43 [==============================] - 51s 1s/step - loss: 0.1952 - accuracy: 0.9347 - val_loss: 0.5999 - val_accuracy: 0.7024\n",
      "Epoch 14/20\n",
      "43/43 [==============================] - 51s 1s/step - loss: 0.1636 - accuracy: 0.9377 - val_loss: 1.1469 - val_accuracy: 0.6131\n",
      "Epoch 15/20\n",
      "43/43 [==============================] - 51s 1s/step - loss: 0.2016 - accuracy: 0.9110 - val_loss: 0.9860 - val_accuracy: 0.5952\n",
      "Epoch 16/20\n",
      "43/43 [==============================] - 52s 1s/step - loss: 0.1921 - accuracy: 0.9214 - val_loss: 0.3815 - val_accuracy: 0.8750\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 32,
   "source": [
    "    # Depthwise Separable Convolution 模型\n",
    "    depthwise_separable_model = build_depthwise_separable_model()\n",
    "    print(\"Training Depthwise Separable Convolution Model...\")\n",
    "    depthwise_separable_history = train_model(depthwise_separable_model, train_generator, validation_generator)\n",
    "    plot_history(depthwise_separable_history, 'Depthwise Separable Convolution')\n",
    "    "
   ],
   "id": "7cbf9146dec8dd21"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T11:09:58.065138Z",
     "start_time": "2024-12-15T11:09:58.025451Z"
    }
   },
   "cell_type": "code",
   "source": "    depthwise_separable_model.save('depthwise_separable_model.h5')",
   "id": "d1920c6435539c38",
   "outputs": [],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T15:35:41.261522Z",
     "start_time": "2024-12-15T15:35:40.936271Z"
    }
   },
   "cell_type": "code",
   "source": [
    "    # Attention Mechanism 模型\n",
    "    attention_model = build_attention_model()\n",
    "    attention_model.summary()"
   ],
   "id": "4339d2e931999db3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 128, 128, 64) 1792        input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 128, 128, 64) 36928       block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_pool (MaxPooling2D)      (None, 64, 64, 64)   0           block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block2_conv1 (Conv2D)           (None, 64, 64, 128)  73856       block1_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2_conv2 (Conv2D)           (None, 64, 64, 128)  147584      block2_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)      (None, 32, 32, 128)  0           block2_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv1 (Conv2D)           (None, 32, 32, 256)  295168      block2_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv2 (Conv2D)           (None, 32, 32, 256)  590080      block3_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv3 (Conv2D)           (None, 32, 32, 256)  590080      block3_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)      (None, 16, 16, 256)  0           block3_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv1 (Conv2D)           (None, 16, 16, 512)  1180160     block3_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv2 (Conv2D)           (None, 16, 16, 512)  2359808     block4_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv3 (Conv2D)           (None, 16, 16, 512)  2359808     block4_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_pool (MaxPooling2D)      (None, 8, 8, 512)    0           block4_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block5_conv1 (Conv2D)           (None, 8, 8, 512)    2359808     block4_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5_conv2 (Conv2D)           (None, 8, 8, 512)    2359808     block5_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block5_conv3 (Conv2D)           (None, 8, 8, 512)    2359808     block5_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block5_pool (MaxPooling2D)      (None, 4, 4, 512)    0           block5_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 8192)         0           block5_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 32)           262176      flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 1)            33          dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 1, 1, 1)      0           dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "multiply_1 (Multiply)           (None, 1, 1, 32)     0           dense_3[0][0]                    \n",
      "                                                                 reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_2 (Lambda)               (None, 1, 32)        0           multiply_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_3 (Lambda)               (None, 1, 32, 1)     0           lambda_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 1)            0           lambda_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 2)            4           global_average_pooling2d_1[0][0] \n",
      "==================================================================================================\n",
      "Total params: 14,976,901\n",
      "Trainable params: 262,213\n",
      "Non-trainable params: 14,714,688\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T15:52:03.336362Z",
     "start_time": "2024-12-15T15:35:43.408717Z"
    }
   },
   "cell_type": "code",
   "source": [
    "    # Compile the model\n",
    "    \n",
    "    print(\"Training Attention Mechanism Model...\")\n",
    "    optimizer = Adam(learning_rate=0.0001)\n",
    "    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.00001)\n",
    "    attention_model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "    print(\"Training Inception Model...\")\n",
    "    attention_history = attention_model.fit(train_generator, epochs=20, validation_data=validation_generator, callbacks=[reduce_lr, early_stopping])"
   ],
   "id": "65831b7de45e0366",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Attention Mechanism Model...\n",
      "Training Inception Model...\n",
      "Epoch 1/20\n",
      "85/85 [==============================] - 50s 586ms/step - loss: 2.9544 - accuracy: 0.5757 - val_loss: 0.6776 - val_accuracy: 0.5952\n",
      "Epoch 2/20\n",
      "85/85 [==============================] - 50s 590ms/step - loss: 0.6794 - accuracy: 0.5964 - val_loss: 0.6783 - val_accuracy: 0.5952\n",
      "Epoch 3/20\n",
      "85/85 [==============================] - 50s 586ms/step - loss: 0.6782 - accuracy: 0.5964 - val_loss: 0.6749 - val_accuracy: 0.5952\n",
      "Epoch 4/20\n",
      "85/85 [==============================] - 49s 573ms/step - loss: 0.6794 - accuracy: 0.5964 - val_loss: 0.6956 - val_accuracy: 0.5952\n",
      "Epoch 5/20\n",
      "85/85 [==============================] - 48s 564ms/step - loss: 0.6984 - accuracy: 0.5786 - val_loss: 0.6820 - val_accuracy: 0.5952\n",
      "Epoch 6/20\n",
      "85/85 [==============================] - 48s 570ms/step - loss: 0.6857 - accuracy: 0.5757 - val_loss: 0.6758 - val_accuracy: 0.5952\n",
      "Epoch 7/20\n",
      "85/85 [==============================] - 49s 573ms/step - loss: 0.8074 - accuracy: 0.5623 - val_loss: 0.6794 - val_accuracy: 0.5952\n",
      "Epoch 8/20\n",
      "85/85 [==============================] - 47s 557ms/step - loss: 0.6808 - accuracy: 0.5846 - val_loss: 0.6749 - val_accuracy: 0.5952\n",
      "Epoch 9/20\n",
      "85/85 [==============================] - 49s 578ms/step - loss: 0.6772 - accuracy: 0.5979 - val_loss: 0.6754 - val_accuracy: 0.5952\n",
      "Epoch 10/20\n",
      "85/85 [==============================] - 47s 553ms/step - loss: 0.6763 - accuracy: 0.5964 - val_loss: 0.6750 - val_accuracy: 0.5952\n",
      "Epoch 11/20\n",
      "85/85 [==============================] - 51s 599ms/step - loss: 0.6764 - accuracy: 0.5964 - val_loss: 0.6749 - val_accuracy: 0.5952\n",
      "Epoch 12/20\n",
      "85/85 [==============================] - 52s 610ms/step - loss: 0.6786 - accuracy: 0.5964 - val_loss: 0.6751 - val_accuracy: 0.5952\n",
      "Epoch 13/20\n",
      "85/85 [==============================] - 51s 604ms/step - loss: 0.6765 - accuracy: 0.5964 - val_loss: 0.6749 - val_accuracy: 0.5952\n",
      "Epoch 14/20\n",
      "85/85 [==============================] - 49s 579ms/step - loss: 0.6753 - accuracy: 0.5964 - val_loss: 0.6750 - val_accuracy: 0.5952\n",
      "Epoch 15/20\n",
      "85/85 [==============================] - 47s 558ms/step - loss: 0.6752 - accuracy: 0.5964 - val_loss: 0.6749 - val_accuracy: 0.5952\n",
      "Epoch 16/20\n",
      "85/85 [==============================] - 47s 556ms/step - loss: 0.6750 - accuracy: 0.5964 - val_loss: 0.6749 - val_accuracy: 0.5952\n",
      "Epoch 17/20\n",
      "85/85 [==============================] - 48s 561ms/step - loss: 0.6748 - accuracy: 0.5964 - val_loss: 0.6749 - val_accuracy: 0.5952\n",
      "Epoch 18/20\n",
      "85/85 [==============================] - 48s 565ms/step - loss: 0.6750 - accuracy: 0.5964 - val_loss: 0.6749 - val_accuracy: 0.5952\n",
      "Epoch 19/20\n",
      "85/85 [==============================] - 49s 572ms/step - loss: 0.6745 - accuracy: 0.5964 - val_loss: 0.6749 - val_accuracy: 0.5952\n",
      "Epoch 20/20\n",
      "85/85 [==============================] - 49s 580ms/step - loss: 0.6746 - accuracy: 0.5964 - val_loss: 0.6749 - val_accuracy: 0.5952\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T13:49:36.045097Z",
     "start_time": "2024-12-15T13:49:35.888680Z"
    }
   },
   "cell_type": "code",
   "source": "    plot_history(attention_history, 'Attention Mechanism')",
   "id": "e0ff7bcde4dbf66",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T11:00:13.124926300Z",
     "start_time": "2024-12-15T07:43:19.772225Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from keras import backend as K\n",
    "import gc\n",
    "\n",
    "K.clear_session()\n",
    "gc.collect()\n",
    " \n",
    "del attention_model\n",
    "del resnet_model\n",
    "del inception_model\n",
    "del depthwise_separable_model"
   ],
   "id": "bdb3d033cbeb500c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20607"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T11:00:13.125930800Z",
     "start_time": "2024-12-15T07:43:19.973483Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from numba import cuda\n",
    "\n",
    "cuda.select_device(0)\n",
    "cuda.close()"
   ],
   "id": "851815fae5168251",
   "outputs": [],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T11:00:13.126911800Z",
     "start_time": "2024-12-15T07:43:26.221107Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "ee27fc764faf88fb",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
